Apparatuses, computer-implemented methods, and computer program products for accurate explosion predicting and warning

ABSTRACT

Embodiments utilize captured data, such as gas data and/or flame/heat data, from sensors in an environment to generate a data-constructed image for use in predicting explosion likelihood within an environment. Some embodiments utilize gas and flame data to generate the data-constructed image that is processable via one or more model(s) to determine whether the environment includes one or more sub-regions at risk of explosion. Some embodiments receive a plurality of gas sensor data and a plurality of flame sensor data, generate a data-constructed image including a plurality of channels based at least in part on such data, and generate explosion prediction data by applying at least a portion of the data-constructed image to a prediction model.

TECHNICAL FIELD

Embodiments of the present disclosure generally are directed toaccurately predicting whether an explosion may occur in an environment,and specifically to utilizing gas sensor data and flame/heat sensor datato accurately predict an explosion may occur in the environment.

BACKGROUND

In various contexts, an environment may be exposed to a possibility ofexplosion. For example, in some contexts, dangerous circumstances maylead to the presence of ingredients that could cause an explosion.Monitoring such ingredients is important to prevent harm to person(s) inan environment and/or the environment itself, however accuratelypredicting a likelihood of explosion and/or time until explosion isuseful in remediating or avoiding such harmful circumstances.

Inventors have discovered problems with current implementations foraccurately predicting explosions. Through applied effort, ingenuity, andinnovation, the inventors have solved many of these problems bydeveloping the solutions embodied in the present disclosure, the detailsof which are described further herein.

BRIEF SUMMARY

In one aspect, a computer-implemented method includes receiving aplurality of gas sensor data and a plurality of flame sensor data,generating a data-constructed image includes a plurality of channels,the plurality of channels includes at least a first channel assignedbased at least in part on the plurality of gas sensor data and at leastone additional channel assigned based at least in part on the pluralityof flame sensor data, and generating explosion prediction data byapplying at least a portion of the data-constructed image to aprediction model, where the prediction model generates the explosionprediction data based at least in part on explosion feature datadetermined from at least the plurality of channels corresponding to atleast a portion of the data-constructed image.

The computer-implemented method may also include thecomputer-implemented method further includes applying thedata-constructed image to a computer vision model that identifies atleast the portion of the data-constructed image determined associatedwith at least one explosion contribution level indicating presence of atleast one explosion contributing factor in an environment, andextracting the portion of the data-constructed image from thedata-constructed image, where the prediction model only processes theextracted portion of the data-constructed image.

The computer-implemented method may also include where the plurality ofgas sensor data includes at least a first gas sensor data portion and asecond gas sensor data portion captured via a first gas sensor, andwhere the plurality of gas sensor data includes a first gas sensor dataportion associated with a first sampling rate, and where the pluralityof flame sensor data includes at least a first flame sensor portioncaptured via a first flame sensor associated with a second samplingrate, where the first sampling rate is faster than the second samplingrate, and where generating the data-constructed image includesgenerating an averaged value by averaging the first gas sensor dataportion and the second gas sensor data portion, and assigning at least afirst pixel of the first channel based at least in part on the averagedvalue.

The computer-implemented method may also include where the plurality ofgas sensor data includes at least a first gas sensor data portioncaptured via a first gas sensor, and where the plurality of gas sensordata includes a first gas sensor data portion associated with a firstsampling rate, and where the plurality of flame sensor data includes atleast a first flame sensor portion and a second flame sensor portioncaptured via a first flame sensor associated with a second samplingrate, where the second sampling rate is faster than the first samplingrate, and where generating the data-constructed image includesgenerating an averaged value by averaging the first flame sensor dataportion and the second flame sensor data portion, and assigning at leasta first pixel of an additional channel of the plurality of additionalchannels based at least in part on the averaged value.

The computer-implemented method may also include where the plurality ofgas sensor data includes a first time series of gas sensor data portionscaptured via at least one gas sensor, and where generating thedata-constructed image includes assigning a first pixel value of thefirst channel based at least in part on a first gas sensor data portionof the first time series of gas sensor data portions corresponding to afirst timestamp, and assigning each subsequent pixel value of the firstchannel based at least in part on a next gas sensor data portionassociated with each subsequent timestamp.

The computer-implemented method may also include where the plurality offlame sensor data includes at least first band range data, second bandrange data, and third band range data, where the at least one additionalchannel includes a second channel, a third channel, and a fourthchannel, and where generating the data-constructed image includesassigning the second channel based at least in part on the first bandrange data, assigning the third channel based at least in part on thesecond band range data, and assigning the fourth channel based at leastin part on the third band range data.

The computer-implemented method may also include thecomputer-implemented method further includes applying thedata-constructed image to a computer vision model that at leastdetermines an explosion contribution level, and determining theexplosion contribution level satisfies a threshold, where the generatingthe explosion likelihood data is initiated in response to determiningthat the explosion contribution level satisfies the threshold.

The computer-implemented method may also include where the plurality ofgas sensor data is collected via a plurality of gas sensors.

The computer-implemented method may also include where the plurality offlame sensor data is collected via a plurality of flame sensors.

The computer-implemented method may also include where the plurality ofgas sensor data includes at least a first gas data portion associatedwith a first gas sensor corresponding to a first environment region anda second gas data portion associated with a second gas sensorcorresponding to a second environment region, and where the plurality offlame sensor data includes a first flame data portion associated with afirst flame sensor corresponding to the first environment region and asecond flame data portion associated with a second flame data portioncorresponding to the second environment region, where generating thedata-constructed image includes generating a first sub-imagecorresponding to the first environment region based at least in part onthe first gas data portion and the first flame data portion, generatinga second sub-image corresponding to the second environment region basedat least in part on the second gas data portion and the second flamedata portion, and generating the data-constructed image by assigning afirst portion of the data-constructed image to the first sub-image andassigning a second portion of the data-constructed image to the secondsub-image.

The computer-implemented method may also include where thedata-constructed image includes a plurality of sub-image, each sub-imagecorresponding to an assigned pixel sub-region of the data-constructedimage.

The computer-implemented method may also include where the explosionprediction data includes a data value indicating a probability of anexplosion.

The computer-implemented method may also include thecomputer-implemented method further includes determining the explosionprediction data satisfies a threshold by at least comparing theexplosion prediction data to the threshold, and in response todetermining the explosion prediction data satisfies the threshold,generating a warning signal.

The computer-implemented method may also include where the predictionmodel includes a specially trained machine learning model. Othertechnical features may be readily apparent to one skilled in the artfrom the following figures, descriptions, and claims.

In one aspect, an apparatus includes at least one processor. Theapparatus also includes at least one memory storing instructions that,when executed by the processor, configure the apparatus to receive aplurality of gas sensor data and a plurality of flame sensor data,generate a data-constructed image includes a plurality of channels, theplurality of channels includes at least a first channel assigned basedat least in part on the plurality of gas sensor data and at least oneadditional channel assigned based at least in part on the plurality offlame sensor data, and generate explosion prediction data by applying atleast a portion of the data-constructed image to a prediction model,where the prediction model generates the explosion prediction data basedat least in part on explosion feature data determined from at least theplurality of channels corresponding to at least a portion of thedata-constructed image.

The apparatus may also be configured where the plurality of flame sensordata includes at least first band range data, second band range data,and third band range data, where the at least one additional channelincludes a second channel, a third channel, and a fourth channel, andwhere generating the data-constructed image includes assign the secondchannel based at least in part on the first band range data, assign thethird channel based at least in part on the second band range data, andassign the fourth channel based at least in part on the third band rangedata.

The apparatus may also be configured where the instructions furtherconfigure the apparatus to apply the data-constructed image to acomputer vision model that at least determines an explosion contributionlevel, and determine the explosion contribution level satisfies athreshold, where the generating the explosion likelihood data isinitiated in response to determining that the explosion contributionlevel satisfies the threshold.

The apparatus may also include where the plurality of gas sensor dataincludes at least a first gas data portion associated with a first gassensor corresponding to a first environment region and a second gas dataportion associated with a second gas sensor corresponding to a secondenvironment region, and where the plurality of flame sensor dataincludes a first flame data portion associated with a first flame sensorcorresponding to the first environment region and a second flame dataportion associated with a second flame data portion corresponding to thesecond environment region, where generating the data-constructed imageincludes generate a first sub-image corresponding to the firstenvironment region based at least in part on the first gas data portionand the first flame data portion, generate a second sub-imagecorresponding to the second environment region based at least in part onthe second gas data portion and the second flame data portion, andgenerate the data-constructed image by assigning a first portion of thedata-constructed image to the first sub-image and assigning a secondportion of the data-constructed image to the second sub-image. Othertechnical features may be readily apparent to one skilled in the artfrom the following figures, descriptions, and claims.

In one aspect, a non-transitory computer-readable storage medium, thecomputer-readable storage medium including instructions that whenexecuted by at least one processor, cause the at least one processor toreceive a plurality of gas sensor data and a plurality of flame sensordata, generate a data-constructed image includes a plurality ofchannels, the plurality of channels includes at least a first channelassigned based at least in part on the plurality of gas sensor data andat least one additional channel assigned based at least in part on theplurality of flame sensor data, and generate explosion prediction databy applying at least a portion of the data-constructed image to aprediction model, where the prediction model generates the explosionprediction data based at least in part on explosion feature datadetermined from at least the plurality of channels corresponding to atleast a portion of the data-constructed image.

The computer-readable storage medium may also include where theplurality of gas sensor data includes at least a first gas data portionassociated with a first gas sensor corresponding to a first environmentregion and a second gas data portion associated with a second gas sensorcorresponding to a second environment region, and where the plurality offlame sensor data includes a first flame data portion associated with afirst flame sensor corresponding to the first environment region and asecond flame data portion associated with a second flame data portioncorresponding to the second environment region, where generating thedata-constructed image includes generate a first sub-image correspondingto the first environment region based at least in part on the first gasdata portion and the first flame data portion, generate a secondsub-image corresponding to the second environment region based at leastin part on the second gas data portion and the second flame dataportion, and generate the data-constructed image by assigning a firstportion of the data-constructed image to the first sub-image andassigning a second portion of the data-constructed image to the secondsub-image. Other technical features may be readily apparent to oneskilled in the art from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 illustrates an example system for explosion predicting inaccordance with at least some example embodiments of the presentdisclosure.

FIG. 2 illustrates an example explosion prediction apparatus inaccordance with at least some example embodiments of the presentdisclosure.

FIG. 3 illustrates an example implementation of a data-constructed imagein accordance with at least some example embodiments of the presentdisclosure.

FIG. 4 illustrates a visualization of an example process forconstructing a channel from a plurality of sub-images in accordance withat least some example embodiments of the present disclosure.

FIG. 5 illustrates an example data-constructed image in accordance withat least some example embodiments of the present disclosure.

FIG. 6 illustrates an example data flow for determining whether toinitiate explosion prediction data generation in accordance with atleast some embodiments of the present disclosure.

FIG. 7 illustrates a process 700 for generating explosion predictiondata in accordance with at least some example embodiments of the presentdisclosure.

FIG. 8 illustrates a process 800 for extracting at least a portion of adata-constructed image by using a computer vision model in accordancewith at least some example embodiments of the present disclosure.

FIG. 9 illustrates a process 900 for assigning a pixel value based on anaveraged value of gas sensor data in accordance with at least someexample embodiments of the present disclosure.

FIG. 10 illustrates a process 1000 for assigning a pixel value based onan averaged value of flame sensor data in accordance with at least someexample embodiments of the present disclosure.

FIG. 11 illustrates a process 1100 for assigning pixel values in asequence over a timestamp interval in accordance with at least someexample embodiments of the present disclosure.

FIG. 12 illustrates a process 1200 for generating a data-constructedimage based at least in part on a plurality of sub-images in accordancewith at least some example embodiments of the present disclosure.

FIG. 13 illustrates a process 1300 for generating a warning signal inaccordance with at least some example embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure now will be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the disclosure are shown. Indeed,embodiments of the disclosure may be embodied in many different formsand should not be construed as limited to the embodiments set forthherein, rather, these embodiments are provided so that this disclosurewill satisfy applicable legal requirements. Like numbers refer to likeelements throughout.

Overview

In various contexts, an environment may become at risk of explosion.Such an explosion (or explosions) in an environment can cause a myriadof problems, including risk to property in the environment, risk to thesafety of users in the environment, or a combination of both. To avoidcatastrophe, predicting whether an explosion may occur in theenvironment can be essential. Accurate predictions become essential inkey moments, such as when a probability of explosion is rapidlyincreasing, thus increasing the risk of harm to person(s) and/orthing(s) in the environment.

In one example context, an environment may be at risk of explosion dueto presence of particular ingredient components that, when combined, cantrigger an explosion. Examples of such ingredients include various gasconcentration(s), heat, spark(s), and/or the like. These ingredients maycombine to cause an explosion, such that the presence of each ingredientbecomes highly relevant to indicating the likelihood of an explosionoccurring. The existence of various ingredients alone, however, may notprovide an accurate representation of a probability of future explosionin certain contexts. For example, the concentration and/or presence of aparticular ingredient rapidly increasing or decreasing may be indicativeof whether the a subsequent explosion is likely.

Attempting to predict an explosion using other analysis may be subjectedto different types of problems. For example, mere image processing ofcaptured images depicting the environment may be exposed to physicalblockages in the environment (e.g., smoke, debris, and/or the like) thatprevent capturing of an image accurately representing the environment.In a circumstance where an image cannot be captured that accuratelyrepresents the environment in question, such implementations can quicklybecome inaccurate or entirely useless. As such, alternativemethodologies that are less exposed to errors are desirable.

Embodiments of the present disclosure provide for improved accuracypredicting of a likelihood of an explosion in an environment. Someembodiments utilize particular data monitored from within theenvironment to construct a particular image, specifically adata-constructed image. The data-constructed image may be specificallyconstructed in a manner that utilizes data indicating presence and/orconcentration of particular explosion contributing factor(s), such aspresence of particular ingredient(s) that contribute to a possibleexplosion in the environment. In this regard, the image representationof such data is processable using one or more image processingtechnique(s) to determine an explosion prediction (e.g., a likelihood,probability, or other indication). In some embodiments, at least aportion of the data-constructed image is processable by one or morespecially trained machine-learning, algorithmic, and/or statisticalmodels that determine particular portions of the environment that areat-risk for explosion, and/or generate explosion prediction dataassociated with a likelihood of an explosion in at least a portion ofthe environment. By utilizing the data-constructed image and imageprocessing methodologies described herein, embodiments of the presentdisclosure generate more accurate predictions as to whether a particularenvironment is likely to explode.

In one example embodiment, gas sensor(s) and flame sensor(s) from withinan environment are utilized to generate a data-constructed image. Thedata-constructed image may be generated such that the data captured fromeach of the sensor(s) is utilized to assign pixel values of a particularchannel of the data-constructed image. For example, gas sensor data froma particular gas sensor, or multiple gas sensors in a particular portionof an environment, may be utilized to capture data sample(s) and/orotherwise generate data value(s) at a particular sampling rate, andassign particular pixel value(s) sequentially within particular portionsof the data-constructed image based on the captured data value(s). Byconstructing data-constructed images with various channels, individualdata types may be assigned to different channels, resulting in a singlemulti-channel image with each of the different relevant data types.Similarly, features may be constructed associated with any and/or allcombinations of channels of the data-constructed image for purposes ofsubsequent image processing.

Embodiments of the present disclosure provide various technicaladvantages. Some embodiments utilize specialized image processing ofdata-constructed image constructed from non-image data captured fromwithin the environment to generate a more accurate prediction thanalternative implementations. Additionally or alternatively, someembodiments utilize data-constructed image(s) constructed from non-imagedata that is significantly more difficult (or impossible) to preventcapture of via the corresponding sensor(s), for example gas and/or flamesensors, thereby increasing the likelihood of being able to accuratelycomplete the process. Additionally or alternatively still, someembodiments utilize particular data, such as gas sensor data and/orflame sensor data, that is captured by existing sensors within theenvironment and/or leveraged for other purposes, such that additionalsensors need not be positioned within the environment for the purpose ofperforming such a prediction. Additionally or alternatively still, someembodiments utilize the multi-channel nature of images to processmultiple relevant data values and/or derive features associated with theindividual values or combination thereof for modeling.

Example Systems and Apparatuses of the Disclosure

FIG. 1 illustrates an example system in which embodiments of the presentdisclosure can operate. Specifically, FIG. 1 depicts an example system100. The system 100 may be utilized to facilitate predicting explosionprediction data, for example representing a likelihood or otherdetermination that an explosion may occur in an environment, using adata-constructed image generated based at least in part on data capturedfrom the environment. As illustrated, the system 100 includes gassensor(s) 102, flame sensor(s) 104, explosion prediction system 108, andalert system(s) 110. The gas sensor(s) 102, flame sensor(s) 104, and/oroptional alert system(s) 110 in some embodiments are located within aparticular environment 106. In some embodiments, the explosionprediction system 108 is located externally from the environment 106,for example in a remote data center, headquarters, or the like.Alternatively, or additionally, in some embodiments, the explosionprediction system 108 is located within the environment 106 (e.g., as anon-premises system). In some embodiments, optionally the components inthe environment 106 (e.g., the gas sensor(s) 102, flame sensor(s) 104,and/or optionally the alert system(s) 110).

The gas sensor(s) 102 includes one or more device(s) that capturesample(s) from an environment, and/or determine value(s) indicatingpresence, concentrations, volumes, and/or the like of one or more gastype(s) from the sample(s). The gas sensor(s) 102 in some embodimentscapture sample(s) at a particular sampling rate, which may be the sameor differ between each gas sensor of the gas sensor(s) 102. In someembodiments, each gas sensor of the gas sensor(s) 102 is configured todetect particular gas type(s), for example a predetermined set of gastype(s). In some embodiments, the gas sensor(s) 102 generate gas sensordata indicating a concentration of oxygen that may contribute to anexplosion, a concentration of one or more fuel gas type(s) that maycontribute to an explosion, and/or the like, or a combination thereof(e.g., capturing multiple data values). In some embodiments, the gassensor(s) 102 includes one or more infrared sensor(s), chemical-resistorsensor(s), and/or the like, to capture and/or otherwise generate gassensor data from each of the gas sensor(s) 102. Non-limiting examples ofgas sensor data captured by the gas sensor(s) 102 includeconcentration(s) of particular gas type(s), indication of whetherparticular gas type(s) is/are present, and/or the like. In someembodiments, the gas sensor(s) 102 include a plurality of gas sensorslocated at different sub-regions of the environment 106. For example, insome embodiments, the gas sensor(s) 102 include at least one gas sensorin each sub-region of a particular environment.

The flame sensor(s) 104 includes one or more devices(s) that capturesample(s) from an environment, and/or determine value(s) indicatingwhether a flame, heat, and/or spark is present within the sample. Insome embodiments, each flame sensor of the flame sensor(s) 104 isconfigured to detect particular data that indicates existence of a flamerepresented in a captured sample. The flame sensor(s) 104 in someembodiments capture sample(s) at a particular sampling rate, which maybe the same or differ between each flame sensor of the flame sensor(s)104. In some embodiments, the flame sensor(s) 104 includes one or moreinfrared, visible light sensors (e.g., photocells), ultraviolet sensors,and/or the like, to capture and/or otherwise generate flame sensor datafrom each of the flame sensor(s) 104. Non-limiting examples of flamesensor data captured by the flame sensor(s) 104 include infrared data,temperature data, and/or the like. In some embodiments, the flamesensor(s) 104 include a plurality of flame sensors located at differentsub-regions of the environment 106. For example, in some embodiments,the flame sensor(s) 104 include at least one flame sensor in eachsub-region of a particular environment.

In one example context, the gas sensor(s) 102 and/or flame sensor(s) 104may be pre-existing within the environment 106 before configurationand/or installation of the associated explosion prediction system 108.For example, the explosion prediction system 108 may leverage thedata-collecting capabilities of any of the exiting gas sensor(s) 102and/or flame sensor(s) 104, such that the explosion prediction system108 is retrofit into the existing system. In this regard, someembodiments described herein for example advantageously perform thedescribed explosion predicting process(es) without requiring anyspecialized sensor(s), and without requiring that the environment 106 beupgraded to include any particular new sensors. It will be appreciatedthat the explosion prediction system 108 may be specially configured toprocess particular data, for example gas sensor data and/or flame sensordata, of a particular type, format, and/or the like that is consistentwith the types of sensors within the environment 106.

The explosion prediction system 108 includes one or more computingdevice(s) embodied in hardware, software, firmware, and/or a combinationthereof, that performs explosion predicting. For example, in someembodiments, the explosion prediction system 108 includes at least oneserver, at least one datastore, and/or a combination thereof. The atleast one server and/or the at least one datastore may be speciallyconfigured via software, hardware, firmware, and/or a combinationthereof, to perform the functionality described herein. In someembodiments, the at least one server includes at least one applicationserver specially configured to execute functionality of at least onesoftware application. Additionally or alternatively, in some embodimentsthe at least one datastore is specially configured to provide datastorage and/or retrieval functionality utilized by the at least onesoftware application. In some embodiments, the explosion predictionsystem 108 provides functionality for performing explosion predicting bygenerating explosion prediction data from captured data. For example, insome embodiments the explosion prediction system 108 receives and/orstores data captured from within the environment 106, for example gassensor data from the gas sensor(s) 102 and/or flame sensor data from theflame sensor(s) 104, and processes such data to perform explosionpredicting by generating explosion prediction data as described furtherherein. In this regard, the explosion prediction system 108 may performsuch explosion predicting utilizing one or mode model(s), such as one ormore computer vision model(s) and/or prediction model(s) as describedherein. Additionally or alternatively, in some embodiments, theexplosion prediction system 108 initiates outputting of alert(s) basedat least in part on the results of such explosion predicting, forexample based at least in part on explosion prediction data.

In some embodiments, the explosion prediction system 108 performsrequesting and/or reception of sensor data from one or more sensor(s)within a particular environment. For example, in some embodiments theexplosion prediction system 108 requests and/or receives gas sensor datafrom the gas sensor(s) 102 and/or flame sensor data from the flamesensor(s) 104, and stores such data indicating which sensor each portionof data was received from and/or a particular sub-region associated withthe data, and/or other metadata utilized for identifying and/orprocessing such data (e.g., timestamp data when data was captured,transmitted, and/or received by the explosion prediction system 108). Insome embodiments, the explosion prediction system 108 utilizes captureddata to generate a data-constructed image, and process thedata-constructed image utilizing one or more model(s) to generateexplosion prediction data and/or perform one or more intermediaryprocessing steps (e.g., determining explosion contribution level(s) fordifferent sub-regions of the environment 106).

In some embodiments, the explosion prediction system 108 includes one ormore display(s), speaker(s), and/or other component(s) or device(s) thatenable output to a user. For example, in some embodiments suchdisplay(s) include a monitor, adaptive touchscreen, and/or the like thatoutputs visual user interface data. Additionally or alternatively, insome embodiments, the explosion prediction system 108 includes one ormore device(s), peripheral(s), and/or other component(s) for receivinguser input, such as for initiating the explosion predicting process(es)described herein. Alternatively or additionally, in some embodiments theexplosion prediction system 108 includes or is communicable with one ormore front-end device(s) (e.g., user device(s)) that enable interactionwith the functionality of the explosion prediction system 108.

In some embodiments, the system 100 includes one or more optional alertsystem(s) 110. The alert system(s) 110 includes one or more computingdevice(s) embodied in hardware, software, firmware, and/or a combinationthereof, that perform outputting of one or more alert(s). Such alert(s)may include visual alert(s), audio alert(s), physical alert(s) or dataalert(s) (e.g., activation of particular systems within theenvironment), and/or the like based on the results of explosionpredicting. In some embodiments, the alert system(s) 110 include analarm system that activates audio sirens, visual flashing warningdevices, speakers, and/or the like, that indicate an explosion isdetermined likely or sufficiently imminent, and/or to evacuate.Additionally or alternatively, in some embodiments the alert system(s)110 includes end user device(s), such that the outputted alert(s)include audio output, user interfaces, notifications, and/or the likevia such end user device(s) to notify maintenance users, administrators,and/or other person(s) associated with the environment 106 of thelikelihood of an explosion in a particular environment, and/or that anexplosion is determined likely or sufficiently imminent. In someembodiments one or more alert system of the alert system(s) 110 is/arelocated within the environment 106, and/or in other embodiments one ormore alert system of the alert system(s) 110 is/are located externalfrom the environment 106, or any combination thereof.

The optional communications network 112 in some embodiments is embodiedin any of a myriad of network configurations. In some embodiments, thecommunications network 112 embodies a public network (e.g., theInternet). In some embodiments, the communications network 112 embodiesa private network (e.g., an internal, localized, or closed-off networkbetween particular devices). In some other embodiments, thecommunications network 112 embodies a hybrid network (e.g., a networkenabling internal communications between particular connected devicesand external communications with other devices). The communicationsnetwork 112 in some embodiments includes one or more base station(s),relay(s), router(s), switch(es), cell tower(s), communications cable(s)and/or associated routing station(s), and/or the like. In someembodiments, the communications network 112 includes one or more usercontrolled computing device(s) (e.g., a user owner router and/or modem)and/or one or more external utility devices (e.g., Internet serviceprovider communication tower(s) and/or other device(s)).

The computing device(s) each may communicate over a whole or a portionof one or more communications networks, such as the communicationsnetwork 112. For example, each of the components of the systemcommunicatively coupled to transmit data to and/or receive data from,for example, one another over the same or different wireless or wirednetworks embodying the communications network 112. Such configuration(s)include, without limitation, a wired or wireless Personal Area Network(PAN), Local Area Network (LAN), Metropolitan Area Network (MAN), WideArea Network (WAN), and/or the like. Additionally, while FIG. 1illustrate certain system entities as separate, standalone entitiescommunicating over the communications network 112, the variousembodiments are not limited to this particular architecture. In otherembodiments, one or more computing entities share one or morecomponents, hardware, and/or the like, or otherwise are embodied by asingle computing device such that connection(s) between the computingentities are over the communications network 112 are altered and/orrendered unnecessary. Alternatively or additionally still, in someembodiments the communications network 112 is embodied by wiredconnections between the gas sensor(s) 102 and/or flame sensor(s) 104,such that a wireless communications network is not required.

FIG. 2 illustrates an example explosion predicting apparatus inaccordance with at least some example embodiments of the presentdisclosure. Specifically, FIG. 2 depicts an example explosion predictingapparatus 200 (“apparatus 200”) specially configured in accordance withat least some example embodiments of the present disclosure. In someembodiments, the explosion prediction system 108 and/or a portionthereof is embodied by one or more system(s), such as the apparatus 200as depicted and described in FIG. 2 . Alternatively or additionally, insome embodiments, a single computing system embodying a combination ofexplosion prediction system 108 and/or alert system(s) 110, such as theapparatus 200 as depicted and described in FIG. 2 . The apparatus 200includes processor 202, memory 204, input/output circuitry 206,communications circuitry 208, optional data monitoring circuitry 210,image management circuitry 212, and prediction processing circuitry 214.In some embodiments, the apparatus 200 is configured, using one or moreof the sets of circuitry 202, 204, 206, 208, 210, 212, and/or 214, toexecute and perform the operations described herein.

In general, the terms computing entity (or “entity” in reference otherthan to a user), device, system, and/or similar words used hereininterchangeably may refer to, for example, one or more computers,computing entities, desktop computers, mobile phones, tablets, phablets,notebooks, laptops, distributed systems, items/devices, terminals,servers or server networks, blades, gateways, switches, processingdevices, processing entities, set-top boxes, relays, routers, networkaccess points, base stations, the like, and/or any combination ofdevices or entities adapted to perform the functions, operations, and/orprocesses described herein. Such functions, operations, and/or processesmay include, for example, transmitting, receiving, operating on,processing, displaying, storing, determining, creating/generating,monitoring, evaluating, comparing, and/or similar terms used hereininterchangeably. In one embodiment, these functions, operations, and/orprocesses can be performed on data, content, information, and/or similarterms used herein interchangeably. In this regard, the apparatus 200embodies a particular, specially configured computing entity transformedto enable the specific operations described herein and provide thespecific advantages associated therewith, as described herein.

Although components are described with respect to functionallimitations, it should be understood that the particular implementationsnecessarily include the use of particular computing hardware. It shouldalso be understood that in some embodiments certain of the componentsdescribed herein include similar or common hardware. For example, insome embodiments two sets of circuitry both leverage use of the sameprocessor(s), network interface(s), storage medium(s), and/or the like,to perform their associated functions, such that duplicate hardware isnot required for each set of circuitry. The use of the term “circuitry”as used herein with respect to components of the apparatuses describedherein should therefore be understood to include particular hardwareconfigured to perform the functions associated with the particularcircuitry as described herein.

Particularly, the term “circuitry” should be understood broadly toinclude hardware and, in some embodiments, software for configuring thehardware. For example, in some embodiments, “circuitry” includesprocessing circuitry, storage media, network interfaces, input/outputdevices, and/or the like. Alternatively or additionally, in someembodiments, other elements of the apparatus 200 provide or supplementthe functionality of another particular set of circuitry. For example,the processor 202 in some embodiments provides processing functionalityto any of the sets of circuitry, the memory 204 provides storagefunctionality to any of the sets of circuitry, the communicationscircuitry 208 provides network interface functionality to any of thesets of circuitry, and/or the like.

In some embodiments, the processor 202 (and/or co-processor or any otherprocessing circuitry assisting or otherwise associated with theprocessor) is/are in communication with the memory 204 via a bus forpassing information among components of the apparatus 200. In someembodiments, for example, the memory 204 is non-transitory and mayinclude, for example, one or more volatile and/or non-volatile memories.In other words, for example, the memory 204 in some embodiments includesor embodies an electronic storage device (e.g., a computer readablestorage medium). In some embodiments, the memory 204 is configured tostore information, data, content, applications, instructions, or thelike, for enabling the apparatus 200 to carry out various functions inaccordance with example embodiments of the present disclosure.

The processor 202 may be embodied in a number of different ways. Forexample, in some example embodiments, the processor 202 includes one ormore processing devices configured to perform independently.Additionally or alternatively, in some embodiments, the processor 202includes one or more processor(s) configured in tandem via a bus toenable independent execution of instructions, pipelining, and/ormultithreading. The use of the terms “processor” and “processingcircuitry” should be understood to include a single core processor, amulti-core processor, multiple processors internal to the apparatus 200,and/or one or more remote or “cloud” processor(s) external to theapparatus 200.

In an example embodiment, the processor 202 is configured to executeinstructions stored in the memory 204 or otherwise accessible to theprocessor. Alternatively or additionally, the processor 202 in someembodiments is configured to execute hard-coded functionality. As such,whether configured by hardware or software methods, or by a combinationthereof, the processor 202 represents an entity (e.g., physicallyembodied in circuitry) capable of performing operations according to anembodiment of the present disclosure while configured accordingly.Alternatively or additionally, as another example in some exampleembodiments, when the processor 202 is embodied as an executor ofsoftware instructions, the instructions specifically configure theprocessor 202 to perform the algorithms embodied in the specificoperations described herein when such instructions are executed.

As one particular example embodiment, the processor 202 is configured toperform various operations associated with explosion predicting, forexample to generate explosion prediction data that accurately representsa likelihood of an explosion in a particular environment. In someembodiments, the processor 202 includes hardware, software, firmware,and/or a combination thereof, that receives and/or stores sensor datafrom one or more sensor(s) associated with an environment. Additionallyor alternatively, in some embodiments, the processor 202 includeshardware, software, firmware, and/or a combination thereof, thatgenerates a data-constructed image from received sensor data.Additionally or alternatively still, in some embodiments, the processor202 includes hardware, software, firmware, and/or a combination thereof,that processes a data-constructed image to determine explosioncontribution level(s). Additionally or alternatively still, in someembodiments, the processor 202 includes hardware, software, firmware,and/or a combination thereof, that determines whether to continue aprocess for explosion predicting. Additionally or alternatively still,in some embodiments, the processor 202 includes hardware, software,firmware, and/or a combination thereof, that generates explosionprediction data based at least in part on at least a portion of adata-constructed image.

In some embodiments, the apparatus 200 includes input/output circuitry206 that provides output to the user and, in some embodiments, toreceive an indication of a user input. In some embodiments, theinput/output circuitry 206 is in communication with the processor 202 toprovide such functionality. The input/output circuitry 206 may compriseone or more user interface(s) and in some embodiments includes a displaythat comprises the interface(s) rendered as a web user interface, anapplication user interface, a user device, a backend system, or thelike. In some embodiments, the input/output circuitry 206 also includesa keyboard, a mouse, a joystick, a touch screen, touch areas, soft keysa microphone, a speaker, or other input/output mechanisms. The processor202 and/or input/output circuitry 206 comprising the processor may beconfigured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 204, and/or the like). In some embodiments, the input/outputcircuitry 206 includes or utilizes a user-facing application to provideinput/output functionality to a client device and/or other displayassociated with a user.

In some embodiments, the apparatus 200 includes communications circuitry208. The communications circuitry 208 includes any means such as adevice or circuitry embodied in either hardware or a combination ofhardware and software that is configured to receive and/or transmit datafrom/to a network and/or any other device, circuitry, or module incommunication with the apparatus 200. In this regard, in someembodiments the communications circuitry 208 includes, for example, anetwork interface for enabling communications with a wired or wirelesscommunications network. Additionally or alternatively in someembodiments, the communications circuitry 208 includes one or morenetwork interface card(s), antenna(s), bus(es), switch(es), router(s),modem(s), and supporting hardware, firmware, and/or software, or anyother device suitable for enabling communications via one or morecommunications network(s). Additionally or alternatively, thecommunications circuitry 208 includes circuitry for interacting with theantenna(s) and/or other hardware or software to cause transmission ofsignals via the antenna(s) or to handle receipt of signals received viathe antenna(s). In some embodiments, the communications circuitry 208enables transmission to and/or receipt of data from a client device,capture device, and/or other external computing device in communicationwith the apparatus 200.

The data monitoring circuitry 210 includes hardware, software, firmware,and/or a combination thereof, that supports various functionalityassociated with capturing, storing, receiving, and/or retrieving sensordata associated with one or more sensor(s) in a particular environment.For example, in some embodiments, the data monitoring circuitry 210includes or is communicable with one or more sensor(s) associated withthe environment, for example gas sensor(s) and/or flame sensor(s).Additionally or alternatively, in some embodiments, the data monitoringcircuitry 210 includes hardware, software, firmware, and/or the like,that receives sensor data from one or more sensor(s), such as gas sensordata and/or flame sensor data. In some embodiments the apparatus 200receives the sensor data in accordance with a particular sampling ratethat the sensor is configured to capture and/or sample from theenvironment. Additionally or alternatively, in some embodiments, thedata monitoring circuitry 210 includes hardware, software, firmware,and/or the like, that stores and/or otherwise maintains the receiveddata for a particular sensor, for example where the sensor data isstored associated with identifier data corresponding to the sensorutilized to capture said data, optional additional data indicating asub-region of the environment where the sensor that captured said datais located, and/or metadata associated with the captured sample.Additionally or alternatively, in some embodiments, the data monitoringcircuitry 210 includes hardware, software, firmware, and/or the like,that processes received data to derive one or more data valuestherefrom, for example averaged value(s), changes in data value(s),and/or the like. In some embodiments, data monitoring circuitry 210includes a separate processor, specially configured field programmablegate array (FPGA), or a specially programmed application specificintegrated circuit (ASIC).

In some embodiments, the data monitoring circuitry 210 of the apparatus200 includes the one or more sensor(s) utilized to capture sensor datafrom the environment. In some such embodiments, the data monitoringcircuitry 210 additionally or alternatively includes hardware, software,firmware, and/or a combination thereof, that activates such sensor(s).It will be appreciated that, in some embodiments, the apparatus 200 doesnot perform the operations for capturing such data from processing.Instead, in some embodiments, the apparatus 200 receives the captureddata for processing, for example from one or more external sensor(s). Inthis regard, in some embodiments the apparatus 200 does not include anysuch sensor(s).

The image management circuitry 212 includes hardware, software,firmware, and/or a combination thereof, that supports variousfunctionality associated with generating and/or maintainingdata-constructed image(s) for further processing as described herein.For example, in some embodiments, the image management circuitry 212includes hardware, software, firmware, and/or any combination thereof,that generates a data-constructed image. Additionally or alternatively,in some embodiments, image management circuitry 212 includes hardware,software, firmware, and/or any combination thereof, that assigns pixelvalues to one or more channels of a data-constructed image based oncaptured data value(s) (e.g., gas sensor data and/or flame sensor data)and/or data value(s) derived therefrom. Additionally or alternatively,in some embodiments, image management circuitry 212 includes hardware,software, firmware, and/or any combination thereof, that generates aplurality of sub-images from captured data value(s). Additionally oralternatively, in some embodiments, image management circuitry 212includes hardware, software, firmware, and/or any combination thereof,that synthesizes a plurality of sub-images to generate adata-constructed image, for example by stitching the plurality ofsub-images at particular pixel sub-regions of the data-constructedimage. Additionally or alternatively, in some embodiments, imagemanagement circuitry 212 includes hardware, software, firmware, and/orany combination thereof, that identify and/or extract particularportion(s) of a data-constructed image, for example pixel sub-region(s)of a data-constructed image based on particular data (e.g., explosioncontribution level(s)). In some embodiments, the image managementcircuitry 212 includes a separate processor, specially configured fieldprogrammable gate array (FPGA), or a specially programmed applicationspecific integrated circuit (ASIC).

The prediction processing circuitry 214 includes hardware, software,firmware, and/or a combination thereof, that supports variousfunctionality associated with determining whether to initiate explosionpredicting process(es) and/or performing such explosion predictingprocess(es). For example in some embodiments, the prediction processingcircuitry 214 includes hardware, software, firmware, and/or acombination thereof, that applies a data-constructed image to computervision model(s) that determine explosion contribution level(s) for oneor more sub-regions of the data-constructed image. In some embodiments,the prediction processing circuitry 214 includes hardware, software,firmware, and/or a combination thereof, that applies thedata-constructed image to at least one computer vision model todetermine explosion contribution level(s) for one or more pixelsub-region(s) of a data-constructed image. Additionally oralternatively, in some embodiments, the prediction processing circuitry214 includes hardware, software, firmware, and/or a combination thereof,that determine whether to initiate and/or otherwise continue anexplosion predicting process based at least in part on determinedexplosion contribution level(s) (e.g., wherein such continuing isinitiated in a circumstance where the explosion contribution level(s)satisfy a particular threshold). Additionally or alternatively, in someembodiments, the prediction processing circuitry 214 includes hardware,software, firmware, and/or a combination thereof, that generatesexplosion prediction data based at least in part on at least a portionof the data-constructed image. In some embodiments, the predictionprocessing circuitry 214 includes hardware, software, firmware, and/or acombination thereof, that applies at least a portion of thedata-constructed image to at least one prediction model to generate theexplosion prediction data. Additionally or alternatively, in someembodiments, the prediction processing circuitry 214 includes hardware,software, firmware, and/or a combination thereof, that generates warningsignal(s) that trigger or otherwise cause outputting of alert(s)associated with the explosion prediction data. In some embodiments, theprediction processing circuitry 214 includes a separate processor,specially configured field programmable gate array (FPGA), or aspecially programmed application specific integrated circuit (ASIC).

Additionally or alternatively, in some embodiments, two or more of thesets of circuitries 202-214 are combinable. Alternatively oradditionally, in some embodiments, one or more of the sets of circuitryperform some or all of the functionality described associated withanother component. For example, in some embodiments, two or more of thesets of circuitry 202-214 are combined into a single module embodied inhardware, software, firmware, and/or a combination thereof. Similarly,in some embodiments, one or more of the sets of circuitry, for examplethe data monitoring circuitry 210, the image management circuitry 212,and/or the prediction processing circuitry 214, is/are combined with theprocessor 202, such that the processor 202 performs one or more of theoperations described above with respect to each of these sets ofcircuitry 210-214.

Example Generation Data-Constructed Image and Processing of theDisclosure

Having described example systems and apparatuses of the disclosure,example visualizations of generation and processing of data-constructedimages will now be discussed. In some embodiments, one or more speciallyconfigured computing device(s) is configured via hardware, software,firmware, and/or any combination thereof, to perform such generationand/or processing of data-constructed images. For example, in someembodiments the explosion prediction system 108 embodied by theapparatus 200 maintains the data environments and/or performs thefunctionality as depicted and described with respect to FIGS. 3-6 .

FIG. 3 illustrates an example implementation of a data-constructed imagein accordance with at least some example embodiments of the presentdisclosure. FIG. 3 depicts an arrangement of data that embodies adata-constructed image, specifically a multi-channel data-constructedimage 310 comprising channels 312-318. In some embodiments, the channels312-318 correspond to RGBa channels of a particular image format. Inother embodiments, it will be appreciated that the channels 312-318 maycorrespond to any custom format, and that a data-constructed image maybe generated of any number of channels. The pixels of each of channels312-318 are arranged based at least in part on captured sensor data,specifically gas sensor data 304 and flame sensor data 308 captured viagas sensor(s) 302 and flame sensor(s) 306 respectively. It will beappreciated that in other embodiments, the data-constructed image 310may be embodied by an alternative arrangement of channels, for example adifferent number of channels and/or a different arrangement of channels.Additionally, or alternatively, in other embodiments thedata-constructed image 310 is generated utilizing an alternativemethodology for assigning the gas sensor data 304 and flame sensor(s)306 to the various channels of the data-constructed image 310.

As illustrated, the gas sensor(s) 302 captures gas sensor data 304. Insome embodiments, the gas sensor data 304 represents gasconcentration(s), indication(s) of presence, and/or the like for one ormore gas type(s). In one example context, the gas sensor data 304includes parts-per-million concentration values for one or more gastypes from samples captured from an environment. In some embodiments,the gas sensor data 304 includes a plurality of portions of gas sensordata, for example representing data value(s) determined forconsecutively captured sample(s) via a gas sensor of the gas sensor(s)302. In some embodiments, the gas sensor data 304 includes averagedvalues for the plurality of gas sensor data portions, for example, suchthat multiple data value(s) for consecutive samples captured at a firstsampling rate are averaged (e.g., with a simple or weighted average) orotherwise manipulated to be associated with a second sampling rate. In acircumstance where the gas sensor(s) 302 capture at a sampling rate of 8ms for example, and the gas sensor data 304 is to be processed at a 16ms sampling rate, two consecutive data value(s) may be averaged todetermine one portion of the gas sensor data 304. Additionally oralternatively, in some embodiments the gas sensor data 304 includesportions of gas sensor data captured via a plurality of gas sensor(s)302 positioned at different locations in an environment, for examplewithin different sub-regions.

As illustrated, the flame sensor(s) 306 captures flame sensor data 308.In some embodiments, the flame sensor data 308 includes data valuesindicating whether a flame is present at a particular sub-region in theenvironment. In one example context, the flame sensor data 308 includesdata for a plurality of bands measured via the flame sensor(s) 306, forexample near-band, wide-band, and/or long-band infrared data sampled viathe flame sensor(s) 306. In some such embodiments, each portion of flamesensor data in the flame sensor data 308 includes a plurality of datavalue(s), for example a near-band data value, a wide-band data value,and/or a long-band data value. In some embodiments, the flame sensordata 308 includes averaged values for a plurality of flame sensor dataportions, for example, such that multiple data value(s) for consecutivesamples captured at a first sampling rate are averaged (e.g., with asimple or weighted average) or otherwise manipulated to be associatedwith a second sampling rate. For example, in a circumstance where theflame sensor(s) 306 captures at a sampling rate of 8 ms, and the flamesensor data 308 is to be processed at a 16 ms sampling rate, twoconsecutive data value(s) may be averaged to determine one portion ofthe flame sensor data 308. Additionally or alternatively, in someembodiments, the flame sensor data 308 includes portions of flame sensordata captured via a plurality of flame sensor(s)s 306 positioned atdifferent locations in an environment, for example within differentsub-regions.

In some embodiments, the apparatus 200, for example, generates thedata-constructed image 310 based at least in part on the gas sensor data304 and flame sensor data 308. For example, in some embodiments theapparatus 200 assigns particular pixel values of the various channels ofthe data-constructed image 310 based at least in part on particular dataof the gas sensor data 304 and/or flame sensor data 308, and/or dataderived therefrom. In some embodiments, the apparatus 200 assigns apixel value of a first channel, such as the channel 312, based at leastin part on the data values of the gas sensor data 304. In someembodiments, the apparatus 200 derives a pixel value utilizing aparticular algorithm that processes the concentration(s) and/or otherdata value(s) represented in a portion of gas sensor data, for exampleto weight particular gas types differently based on their likelihood tocontribute to an explosion. In some embodiments, particular gas typesthat are more likely to contribute to an explosion are assigned a higherweight than the gas types that are less likely to contribute to anexplosion, such that a single pixel value is determinable by averagingthe concentrations of the gas sensor data utilizing the weights for eachcorresponding gas type. In this regard, the apparatus 200 may utilizeportions of gas sensor data captured by a particular gas sensor of thegas sensor(s) 302 to assign subsequent pixel values of the channel 312until the channel is fully assigned. Alternatively or additionally, insome embodiments the apparatus 200 averages data values from the gassensor data 304 to derive a pixel value for a particular pixel, forexample by averaging portions of gas sensor data 304 that are capturedby a gas sensor over multiple samples, and/or that average portions ofgas sensor data 304 captured by a plurality of gas sensors that are allassociated with the same sub-region of an environment. In someembodiments, pixels of the channel 312 are arranged sequentially (e.g.,starting from a (0, 0) pixel coordinate) to fill the channel 312 and/ora portion thereof.

In some embodiments, different portions of the gas sensor data 304 areutilized to assign pixel(s) at different pixel sub-regions of thechannel 312. For example, in some embodiments, a first portion of thegas sensor data 304 captured via gas sensor(s) of the gas sensor(s) 302from a first sub-region of the environment are utilized to assign pixelsof a first pixel sub-region corresponding to the first sub-region of theenvironment, a second portion of the gas sensor data 304 captured viagas sensor(s) of the gas sensor(s) 302 from a second sub-region of theenvironment are utilized to assign pixels of a second pixel sub-regioncorresponding to the second sub-region of the environment, and/or thelike for any number of sub-regions.

The remaining channels channel 314, 316, and 318 may similarly beassigned based on the data value(s) of the flame sensor data 308. Insome embodiments, the apparatus 200 assigns pixel values of a pluralityof channels based at least in part on data values for the differentfrequency bands for each portion of the flame sensor data 308. In someembodiments, the apparatus 200 assigns a pixel value of a secondchannel, such as the channel 314, based at least in part on data valuesfor a first band of the flame sensor data 308, assigns a pixel value ofa third channel, such as the channel 316, based at least in part on datavalues for a second band of the flame sensor data 308, and assigns apixel value of a fourth channel, such as the channel 318, based at leastin part on data values for a third band of the flame sensor data 308. Inone example context, the apparatus 200 assigns pixel values for channel314 based at least in part on near-band data values of an IR reading viaa flame sensor of the flame sensor(s) 306, assigns pixel values forchannel 316 based at least in part on wide-band data values of the IRreading via the flame sensor of the flame sensor(s) 306, and assignspixel values for channel 318 based at least in part on long-band datavalues of the IR reading via the flame sensor of the flame sensor(s)306. In some embodiments, the apparatus 200 derives a pixel value foreach of the channels utilizing a particular algorithm that processes thedata value for the band corresponding to the channel. In this regard,the apparatus 200 may utilize portions of flame sensor data captured bya particular flame sensor of the flame sensor(s) 306 to assignsubsequent pixel values of any one of the channels 314, 316, and/or 318,until the channel is fully assigned. Alternatively or additionally, insome embodiments the apparatus 200 averages data values from the flamesensor data 308 to derive a pixel value for a particular pixel, forexample by averaging portions of flame sensor data 308 that are capturedby a gas sensor over multiple samples, and/or that average portions offlame sensor data 308 captured by a plurality of flame sensors that areall associated with the same sub-region of an environment. In someembodiments, pixels of the channel 314, 316, and/or 318 are arrangedsequentially (e.g., starting from a (0, 0) pixel coordinate) to fill thechannel and/or a portion thereof.

It will be appreciated that in other embodiments, the gas sensor dataand/or flame sensor data is utilized to generate and/or otherwise assigndata to any other number of channels. For example, in some embodiments,different portions of gas sensor data (e.g., associated with differentgas types) is utilized to assign pixel values to a plurality ofchannels. Alternatively or additionally, in some embodiments flamesensor data is utilized to assign only a single channel, or a differentnumber of channels (e.g., two channels, four or more channels, and/orthe like). It will be appreciated that the channels available and/orutilized for processing via the data-constructed image may be generatedas available and/or as needed. In this regard, the particular depictedexample data-constructed image should not limit the scope or spirit ofthis disclosure.

FIG. 4 illustrates a visualization of an example process forconstructing a channel from a plurality of sub-images in accordance withat least some example embodiments of the present disclosure. It shouldbe appreciated that any of the channels 312-318 may be generated asdescribed with respect to FIG. 4 . In some embodiments, all channels ofa particular image are generated utilizing the sub-imaging method asdepicted and described herein. In some other embodiments, only somechannels of the particular image are generated utilizing the sub-imagingmethod as depicted and described herein. Additionally, though a channelis depicted as being generated utilizing gas sensor data, it will beappreciated that the sub-imaging method described herein may similarlybe utilized to generate one or more channel(s) based at least in part onflame sensor data.

In some embodiments, a particular environment includes a plurality ofsub-regions. For example, in some embodiments, an environment includes aplurality of sub-regions defining different rooms of the environment,different areas separated by a particular distance, different areasseparated by particular physical element(s), and/or the like. It will beappreciated that an environment may be sub-divided into any number ofsub-regions, which may be of the same size or of different sizes.

As illustrated, each sub-region of a plurality of sub-regions for aparticular environment each include one or more gas sensor(s). Forexample, the environment sub-region 406 a is associated with gassensor(s) 402 a, environment sub-region 406 b is associated with gassensor(s) 402 b, environment sub-region 406 c is associated with gassensor(s) 402 c, and environment sub-region 406 d is associated withenvironment sub-region 406 d. In some embodiments, each gas sensor ofthe gas sensor(s) 402 a-402 d is located within its correspondingsub-region of the environment. Additionally or alternatively, in someembodiments the apparatus 200 maintains data indicating the associationbetween a particular gas sensor and its corresponding sub-region of theenvironment, such that gas sensor data received from the correspondinggas sensor may be processed associated with a particular sub-region ofthe environment. In some embodiments, the apparatus 200 stores dataassociating an identifier a particular gas sensor with a data identifieridentifying the corresponding sub-region of the environment.

As illustrated, each of the gas sensor(s) captures one or more portionsof gas sensor data. For example, in some embodiments gas sensor(s) 402 acaptures gas sensor data 404 a, which is associated with environmentsub-region 406 a. Similarly, gas sensor(s) 402 b captures 404 b, whichis associated with environment sub-region 406 b, and so on. In some suchembodiments, the apparatus 200 associates each of the gas sensor data404 a, gas sensor data 404 b, gas sensor data 404 c, and gas sensor data404 d with the corresponding sub-region of the environment based atleast in part on one or more data identifier(s), for example identifyingthe sensor that captured the particular portion of gas sensor data. Inthis regard, the apparatus 200 may maintain such portions of gas sensordata with the corresponding environment sub-regions for furtherprocessing.

The apparatus 200 generates a plurality of sub-images based on thedifferent sets of gas sensor data. For example, in some embodiments, theapparatus 200 generates a sub-image for each region of the environmentfor which data was captured. As illustrated, the apparatus 200 generatesa first sub-image 410 a corresponding to the environment sub-region 406a, a second sub-image 410 b corresponding to environment sub-region 406b, a third sub-image 410 c corresponding to environment sub-region 406c, and a fourth sub-image 410 d corresponding to environment sub-region406 d. Each of the sub-images may be generated based on the set of gassensor data corresponding to that particular sub-region of theenvironment, for example the gas sensor data 404 a corresponding tosub-image 410 a, gas sensor data 404 b corresponding to sub-image 410 b,gas sensor data 404 c corresponding to sub-image 410 c, and gas sensordata 404 d corresponding to sub-image 410 d.

It should be appreciated that the pixels of any particular sub-image maybe assigned as described above with respect to FIG. 3 . For example, thepixel values of the sub-image 410 a may be assigned sequentially basedat least in part on the data values of the corresponding gas sensor data404 a, or data value(s) derived therefrom. Pixel values may be assignedto each pixel of the sub-images until each sub-image is filled. Forexample, the pixel values of the sub-image 410 b may be assignedsequentially based at least in part on the data values of thecorresponding gas sensor data 404 b, and so on.

In some embodiments, the apparatus 200 generates the channel 408 basedat least in part on the plurality of sub-images once assignment of thepixel values for such sub-images are complete. In this regard, in someembodiments each sub-image corresponds to a pixel sub-region of thechannel 408. In some embodiments, the pixel sub-region corresponding toa particular sub-region is predetermined, for example based at least inpart on the corresponding sub-region of an environment corresponding tothat sub-image. For example, as illustrated, the sub-image 410 acorresponds to a top-left pixel sub-region of the channel 408, sub-image410 b corresponds to a bottom-right pixel sub-region of the channel 408,sub-image 410 c corresponds to a bottom-left pixel sub-region of thechannel 408, and sub-image 410 d corresponds to a top-right pixelsub-region of the channel 408. In some embodiments, the apparatus 200stitches the various sub-images in alignment with each sub-image'scorresponding pixel sub-region. In this regard, the resulting channel408 includes all pixel values for all sub-regions.

Such generation of a channel image utilizing sub-images may be repeatedfor any number of channels. For example, in some embodiments theapparatus 200 repeats the process for generating a plurality of channelsutilizing different data, for example gas sensor data for a firstchannel, a first portion of flame sensor data for a second channel, asecond portion of the flame sensor data for a third channel, and a thirdportion of flame sensor data for a fourth channel. Additionally oralternatively, in some embodiments, the apparatus 200 generates adata-constructed image by layering the channels upon completion ofgenerating of each channel image.

FIG. 5 illustrates an example data-constructed image in accordance withat least some example embodiments of the present disclosure.Specifically, FIG. 5 depicts an example data-constructed image 500. Insome embodiments, the data-constructed image 500 includes a plurality ofpixel sub-region, for example corresponding to different sub-regions ofa particular environment represented by the data-constructed image 500.

As illustrated, the pixels of the data-constructed image 500 may berepresented by the pixel values of each channel of the data-constructedimage 500. In one example context, for example where thedata-constructed image 500 is represented in RGBa format, each pixel isdepicted in the data-constructed image 500 as a color value having aparticular transparency, for example determined based on the pixel valueat each channel. In some embodiments, for example where the pixel valuesare assigned as subsequent samples are captured via one or moresensor(s), the data-constructed image 500 may depict changes in the datavalues measured via the captured samples. In this regard, thedata-constructed image 500 may represent a rate of change of themeasured value corresponding to such sensors.

It will be appreciated that the data-constructed image 500 may berepresented in a known image format (e.g., RGBa as described herein). Inthis regard, the data-constructed image 500 may advantageously beprocessed in any of a myriad of known manners utilized to process suchimages. For example, in some embodiments, known image processingalgorithms, machine learning models, and/or the like may be utilized toprocess the data-constructed image 500, and/or specially-configuredalgorithms, machine learning models, and/or the like may be utilized, asdescribed further herein. Additionally or alternatively, thedata-constructed image 500 may be outputted for displaying via one ormore user interface(s). In some embodiments, the data-constructed imageis not processable in a manner readily interpretable by a user and/orrenderable via one or more user interface(s), for example where thedata-constructed image includes more channels than known image formatsreadily interpret.

FIG. 6 illustrates an example data flow for determining whether toinitiate explosion prediction data generation in accordance with atleast some embodiments of the present disclosure. In some embodiments,the apparatus 200 processes data in accordance with the data flowdepicted and described. The apparatus 200 may perform such a data flowalone or in combination with one or more other device(s), including oneor more user device(s), sensor(s), and/or the like.

As illustrated, the apparatus 200 (for example), retrieves, maintains,and/or otherwise includes the data-constructed image 608. In someembodiments, the apparatus 200 generates the data-constructed image 608as depicted and described herein, for example with respect to FIGS. 3-5.

In some embodiments, the apparatus 200 applies the data-constructedimage 608 to a computer vision model 610. In some embodiments, thecomputer vision model 610 embodies a specially trained machine-learningmodel and/or AI model that processes image data, for example image datain the format of the data-constructed image 608. For example, in someembodiments the computer vision model 610 is trained such that the modellearns the features of each image that correspond to each explosioncontribution level, as described further herein. In some embodiments,the computer vision model 610 embodies a machine-learning model trainedutilizing a supervised learning method or unsupervised learning method.In some embodiments, the computer vision model 610 is specially trainedto extract particular explosion feature data from inputted image data.In this regard, in some embodiments the computer vision model 610 isspecially trained to generate particular output based at least in parton the explosion feature data.

As illustrated, the computer vision model 610 outputs explosioncontribution level(s) 612. In some embodiments, the explosioncontribution level(s) 612 includes data values indicating whetherparticular explosion contributing factor(s) were sufficiently detectedfrom the data-constructed image 608. In some embodiments, the computervision model 610 outputs explosion contribution level(s) 612 indicatinga selected level from a plurality of candidate explosion contributionlevels. In one example context, the computer vision model 610 outputs aselected explosion contribution level from a plurality of candidateexplosion contribution levels including a first explosion contributionlevel indicating a normal or ambient environment (e.g., with no detectedexplosion contributing factors or less than a particular threshold), asecond explosion contribution level indicating a gas leak is detected(e.g., gas concentration(s) above a particular threshold), and/or athird explosion contribution level indicating a flame or sufficient heatenergy for explosion is detected (e.g., heat or flame indications abovea particular threshold). In some embodiments, the explosion contributionlevels are tiered, such that a third explosion contribution levelrequires all factors of a previous second explosion contribution level(e.g., where the second explosion contribution level represents presenceof a gas leak, and the third explosion contribution level representspresence of a gas leak and heat energy or flame).

In some embodiments, the computer vision model 610 generates anexplosion contribution level for different portions of thedata-constructed image 608. For example, in some embodiments, thecomputer vision model 610 generates an independent explosioncontribution level for each pixel sub-region of the data-constructedimage 608. In this regard, the computer vision model 610 may indicatethe particular pixel sub-regions that are associated with a risk ofexplosion, as represented the explosion contribution level(s) 612.Similarly, based on the explosion contribution level of the explosioncontribution level(s) 612 for each of the pixel sub-regions, thecorresponding sub-regions of the environment represented by thedata-constructed image 608 may be identified based on such explosioncontribution level(s) 612 and associations between the pixel sub-regionsand sub-regions of the environment. In some such contexts, theidentification of sub-regions of an environment particularly at risk forexplosion enables processing to be focused only on data associated withsuch sub-regions in particular, for example in the subsequent operationsas described herein.

In some embodiments, the apparatus 200 determines whether to continue aprocess for explosion predicting based at least in part on the explosioncontribution level(s) 612. For example in some embodiments, theapparatus 200 determines whether each of the explosion contributionlevel(s) 612 satisfies a particular threshold. In some embodiments, theapparatus 200 the threshold indicates a maximum level indicating that aparticular environment (or sub-region thereof) is sufficiently not atrisk of explosion. For example, in the example context where theexplosion contribution level(s) 612 are each selected from a firstexplosion contribution level indicating a normal or ambient environment,a second explosion contribution level indicating a gas leak is detected,and/or a third explosion contribution level indicating a flame orsufficient heat energy for explosion is detected, the threshold mayindicate that only the first explosion contribution level indicatingnormal ambient conditions is sufficient to cease the processes forexplosion predicting.

In this regard, the apparatus 200 may compare the explosion contributionlevel from explosion contribution level(s) 612 for the environment, orindividual pixel sub-regions thereof, with the threshold to determinewhether the threshold is exceeded (e.g., whether the explosioncontribution level is other than a normal level, in one examplecontext). In circumstances where the apparatus 200 determines thethreshold is satisfied (e.g., the level for a particular sub-region isnormal), the apparatus 200 may proceed to perform block 604. At block604, the apparatus 200 may continue monitoring for sensor data, but doesnot continue explosion prediction data generation for the environmentand/or particular sub-region of the environment. In this regard, theapparatus 200 may forego further processing of the data associated withthe particular corresponding sub-regions until subsequent data indicatesa change in the corresponding explosion contribution level thatsatisfies the threshold at decision block 602. In circumstances wherethe apparatus 200 determines the threshold is not satisfied (e.g., thelevel for a particular sub-region is not normal or otherwise aboveanother threshold), the apparatus 200 may proceed to perform block 606.At block 606, the apparatus 200 continues explosion prediction datageneration for the environment, or at least the sub-region correspondingto the explosion contribution level that satisfied the threshold. Forexample, in some embodiments the apparatus 200 generates the explosionprediction data for one or more sub-regions by applying at least aportion of the data-constructed image 608 to a prediction model asdescribed further herein.

It should be appreciated that the process described herein for receivingsensor data, processing sensor data to generate a data-constructedimage, and processing the data-constructed image to determine whether tocontinue explosion prediction data generation may be repeated any numberof times. In this regard, the apparatus 200, for example, maycontinually perform such a process to continually monitor a particularenvironment and detect when the environment, or particular sub-regionsthereof, may be at risk of explosion. Such determinations as depictedand described with respect to FIG. 6 advantageously prevent unnecessarywaste of computing resources for processing data associated with anenvironment or sub-region thereof in circumstances where particularexplosion contributing factors for explosion are not determinedsufficiently present with reduced processing.

Example Processes of the Disclosure

Having described example systems and apparatuses, data-constructedimages and related data flows in accordance with the disclosure, exampleprocesses of the disclosure will now be discussed. It will beappreciated that each of the flowcharts depicts an examplecomputer-implemented process that is performable by one or more of theapparatuses, systems, devices, and/or computer program productsdescribed herein, for example utilizing one or more of the speciallyconfigured components thereof.

The blocks indicate operations of each process. Such operations may beperformed in any of a number of ways, including, without limitation, inthe order and manner as depicted and described herein. In someembodiments, one or more blocks of any of the processes described hereinoccur in-between one or more blocks of another process, before one ormore blocks of another process, in parallel with one or more blocks ofanother process, and/or as a sub-process of a second process.Additionally or alternatively, any of the processes in variousembodiments include some or all operational steps described and/ordepicted, including one or more optional blocks in some embodiments.With regard to the flowcharts illustrated herein, one or more of thedepicted block(s) in some embodiments is/are optional in some, or all,embodiments of the disclosure. Optional blocks are depicted with broken(or “dashed”) lines. Similarly, it should be appreciated that one ormore of the operations of each flowchart may be combinable, replaceable,and/or otherwise altered as described herein.

FIG. 7 illustrates a process 700 for generating explosion predictiondata in accordance with at least some example embodiments of the presentdisclosure. In some embodiments, the process 700 is embodied by computerprogram code stored on a non-transitory computer-readable storage mediumof a computer program product configured for execution to perform theprocess as depicted and described. Alternatively or additionally, insome embodiments, the process 700 is performed by one or more speciallyconfigured computing devices, such as the apparatus 200 alone or incommunication with one or more other component(s), device(s), system(s),and/or the like. In this regard, in some such embodiments, the apparatus200 is specially configured by computer-coded instructions (e.g.,computer program instructions) stored thereon, for example in the memory204 and/or another component depicted and/or described herein and/orotherwise accessible to the apparatus 200, for performing the operationsas depicted and described. In some embodiments, the apparatus 200 is incommunication with one or more external apparatus(es), system(s),device(s), and/or the like, to perform one or more of the operations asdepicted and described. For example, the apparatus 200 in someembodiments is in communication with an end-user computing device,sensor(s), alert system(s), and/or the like. For purposes of simplifyingthe description, the process 700 is described as performed by and fromthe perspective of the apparatus 200.

The process 700 begins at operation 702. In operation 702, process 700receives a plurality of gas sensor data and a plurality of flame sensordata. The plurality of gas sensor data may include any number of gassensor data portions captured from an environment, and/or the pluralityof flame sensor data may include any number of flame sensor dataportions captured from the environment. In some embodiments, theapparatus 200 receives the gas sensor data from one or more gassensor(s) in an environment, and/or receives the flame sensor data fromone or more flame sensor(s) in an environment. In some embodiments,different portions of the plurality of gas sensor data are associatedwith different sub-regions of an environment, and/or different portionsof the plurality of flame sensor data are associated with differentsub-regions of an environment. In some such embodiments, the differentportions are associated with different sub-regions based at least inpart on data indicating the sensor that captured such data.

In operation 704, process 700 generates a data-constructed imagecomprising a plurality of channels, the plurality of channels comprisingat least a first channel assigned based at least in part on theplurality of gas sensor data and at least one additional channelassigned based at least in part on the plurality of flame sensor data.In some embodiments, the apparatus 200 for example assigns a firstchannel based at least in part on the gas concentration values of theplurality of gas sensor data, and assigns a second channel, thirdchannel, and fourth channel of the additional channels based at least inpart on different bands of data represented in each portion of theplurality of flame sensor data. In some embodiments, the apparatus 200generates the data-constructed image by generating a plurality ofsub-images, for example associated with different sub-regions of anenvironment, and combining or otherwise stitching such sub-imagestogether in accordance with defined pixel sub-regions corresponding toeach sub-image. In some embodiments, the pixel sub-regions arepredefined, such that pixels in particular pixel sub-regions are alwaysassigned based on data captured from the same sensors.

In optional operation 706, process 700 applies the data-constructedimage to a computer vision model that at least determines an explosioncontribution level. In some embodiments, the computer vision modelcomprises a specially trained deep-learning, AI, or othermachine-learning, statistical, or algorithmic model that is trained todetermine explosion feature data from the data-constructed image andutilizes such explosion feature data to output one or more explosioncontribution level(s). In some embodiments, an explosion contributionlevel is output corresponding to predefined pixel sub-regions of thedata-constructed image, for example corresponding to particularsub-regions of a corresponding environment. Alternatively oradditionally, in some embodiments, the computer vision model defines theparticular pixel sub-region of the data-constructed image that isassociated with a non-normal explosion contribution level.

In optional operation 708, process 700 determines that the explosioncontribution level does not satisfy a threshold. For example, in someembodiments, the apparatus 200 compares the determined explosioncontribution level with a threshold explosion contribution levelindicating normal, such that the threshold is satisfied in circumstanceswhere the determined explosion contribution level matches the thresholdexplosion contribution level. Alternatively or additionally, in someembodiments, the apparatus 200 determines the threshold is not satisfiedin a circumstance where the determined explosion contribution level isnot the same as the threshold explosion contribution level (e.g., notnormal). It will be appreciated that in some embodiments, a plurality ofexplosion contribution levels corresponding to a plurality of portionsof the data-constructed image are compared to the threshold, andprocessing continues utilizing only those portions that are determinednot to satisfy the threshold.

In operation 710, process 700 generates explosion prediction data byapplying at least a portion of the data-constructed image to aprediction model. In some embodiments, the prediction model comprises aspecially-trained machine-learning model, AI, algorithmic, and/orstatistical model that generates explosion prediction data based atleast in part on input data. In some embodiments, the prediction modelreceives image data as input, for example at least a portion of adata-constructed image. The prediction model may then process allchannels of the inputted image data to generate the correspondingexplosion prediction data as output. In some embodiments, the apparatus200 extracts particular portions of the data-constructed image that areassociated with an explosion contribution level that does not satisfythe threshold (e.g., pixel sub-regions that are associated with anon-normal explosion contribution level). The extracted portions of thedata-constructed image may subsequently be provided as input to theprediction model.

It should be appreciated that the prediction model may be speciallytrained to output any of a myriad of output types. In some embodiments,the explosion prediction data embodies a probability indicating alikelihood of explosion in an environment or a particular sub-regionthereof. Alternatively or additionally, in some embodiments theexplosion prediction data embodies data indicating a Boolean value ofwhether an explosion in the environment or a particular sub-regionthereof is determined sufficiently likely.

FIG. 8 illustrates a process 800 for extracting at least a portion of adata-constructed image by using a computer vision model in accordancewith at least some example embodiments of the present disclosure. Insome embodiments, the process 800 is embodied by computer program codestored on a non-transitory computer-readable storage medium of acomputer program product configured for execution to perform the processas depicted and described. Alternatively or additionally, in someembodiments, the process 800 is performed by one or more speciallyconfigured computing devices, such as the apparatus 200 alone or incommunication with one or more other component(s), device(s), system(s),and/or the like. In this regard, in some such embodiments, the apparatus200 is specially configured by computer-coded instructions (e.g.,computer program instructions) stored thereon, for example in the memory204 and/or another component depicted and/or described herein and/orotherwise accessible to the apparatus 200, for performing the operationsas depicted and described. In some embodiments, the apparatus 200 is incommunication with one or more external apparatus(es), system(s),device(s), and/or the like, to perform one or more of the operations asdepicted and described. For example, the apparatus 200 in someembodiments is in communication with an end-user computing device,sensor(s), alert system(s), and/or the like. For purposes of simplifyingthe description, the process 800 is described as performed by and fromthe perspective of the apparatus 200.

The process 800 begins at operation 802. In some embodiments, theprocess 800 begins after one or more operations depicted and/ordescribed with respect to any one of the other processes describedherein. For example, in some embodiments as depicted, the process 800begins after execution of operation 704. In this regard, some or all ofthe process 800 may replace or supplement one or more blocks depictedand/or described with respect to any of the processes described herein.Upon completion of the process 800, the flow of operations mayterminate. Additionally or alternatively, as depicted, upon completionof the process 800 in some embodiments, flow may return to one or moreoperation(s) of another process, such as the operation 706. It will beappreciated that, in some embodiments, the process 800 embodies asub-process of one or more other process(es) depicted and/or describedherein, for example the process 700.

In operation 802, the process 800 applies the data-constructed image toa computer vision model that identifies at least the portion of thedata-constructed image determined associated with at least one explosioncontribution level indicating presence of at least one explosioncontributing factor in at least a portion of an environment. In someembodiments, for example, the apparatus 200 utilizes the computer visionmodel to process different pixel sub-regions of the data-constructedimage, and determine the pixel sub-regions comprising at least one pixelassociated with an identified feature in the image that indicatespresence of at least one explosion contributing factor. In someembodiments, the apparatus 200 identifies pixel sub-regions from apredetermined set of defined pixel sub-regions. In other embodiments,the apparatus 200 utilizes the computer vision model to identify theparticular pixels that define a pixel sub-region.

In operation 804, the process 800 extracts the portion of thedata-constructed image from the data-constructed image, where theprediction model only processes the extracted portion of thedata-constructed image. The computer vision model may process thedata-constructed image as a whole and in some embodiments extracts alldefined pixel sub-regions corresponding to at least one identifiedfeature in the data-constructed image. In some embodiments, theapparatus 200 extracts all portions of the data-constructed image thatare associated with an explosion contribution level that does notsatisfy a threshold. Alternatively or additionally, in some embodiments,the apparatus 200 extracts particular pixels identified by the computervision model as associated with a particular feature, for exampleindicating presence of at least one explosion contributing factor.Alternatively or additionally, in some embodiments, the apparatus 200extracts one or more portion(s) of the data-constructed image that arenot associated with an explosion contribution level that does satisfy athreshold (e.g., indicating no explosion contribution factor is detectedin that region) in a circumstance where a neighboring region includesdata at or near (e.g., within a relevant distance threshold as definedby a number of pixels, for example) the neighboring region. It will beappreciated that in some embodiments, the pixel sub-regions extractedfrom the data-constructed image is/are stored to a new image dataobject, file, and/or the like.

In some embodiments, the extracted portions of the data-constructedimage are subsequently processed by the apparatus 200. For example, insome embodiments, only the extracted portions are inputted as the atleast a portion of the data-constructed image to the prediction model,as described herein with respect to operation 708.

FIG. 9 illustrates a process 900 for assigning a pixel value based on anaveraged value of gas sensor data in accordance with at least someexample embodiments of the present disclosure. In some embodiments, theprocess 900 is embodied by computer program code stored on anon-transitory computer-readable storage medium of a computer programproduct configured for execution to perform the process as depicted anddescribed. Alternatively or additionally, in some embodiments, theprocess 900 is performed by one or more specially configured computingdevices, such as the apparatus 200 alone or in communication with one ormore other component(s), device(s), system(s), and/or the like. In thisregard, in some such embodiments, the apparatus 200 is speciallyconfigured by computer-coded instructions (e.g., computer programinstructions) stored thereon, for example in the memory 204 and/oranother component depicted and/or described herein and/or otherwiseaccessible to the apparatus 200, for performing the operations asdepicted and described. In some embodiments, the apparatus 200 is incommunication with one or more external apparatus(es), system(s),device(s), and/or the like, to perform one or more of the operations asdepicted and described. For example, the apparatus 200 in someembodiments is in communication with an end-user computing device,sensor(s), alert system(s), and/or the like. For purposes of simplifyingthe description, the process 800 is described as performed by and fromthe perspective of the apparatus 200.

The process 900 begins at operation 902. In some embodiments, theprocess 900 begins after one or more operations depicted and/ordescribed with respect to any one of the other processes describedherein. For example, in some embodiments as depicted, the process 900begins after execution of operation 702. In this regard, some or all ofthe process 900 may replace or supplement one or more blocks depictedand/or described with respect to any of the processes described herein.Upon completion of the process 900, the flow of operations mayterminate. Additionally or alternatively, as depicted, upon completionof the process 900 in some embodiments, flow may return to one or moreoperation(s) of another process, such as the operation 704. It will beappreciated that, in some embodiments, the process 900 embodies asub-process of one or more other process(es) depicted and/or describedherein, for example the process 700.

In operation 902, the process 900 generates an averaged value. In someembodiments, the apparatus 200 generates the averaged value by averagingthe first gas sensor data portion and the second gas sensor dataportion. In some embodiments, the averaged value comprises a simpleaverage of a plurality of gas sensor data portions, including at leastthe first gas sensor data portion and the second gas sensor dataportion. Alternatively or additionally, in some embodiments, theaveraged value comprises a weighted average of a plurality of gas sensordata portions, including at least the first gas sensor data portion andthe second gas sensor data portion.

In some embodiments, the first gas sensor data portion and the secondgas sensor data portion correspond to sequentially captured samples. Forexample, the second gas sensor data portion may be captured after thefirst gas sensor data portion by a particular gas sensor. In some suchcontexts, the averaged value may be generated corresponding to adifferent sampling rate than the sampling rates corresponding toindividual first and second gas sensor data portions. Alternatively oradditionally, in some embodiments, the first gas sensor data portion andthe second gas sensor data portion are captured by different gas sensorsassociated with the same environment or sub-region of the environment.In some such embodiments, all gas sensor data portions from gas sensorscorresponding to the sub-region may be averaged to determine a value forthe sub-region associated with a particular sampling time (e.g.,corresponding to samples captured at the same timestamp or within aparticular interval from one another).

In operation 904, the process 900 assigns at least a first pixel of thefirst channel based at least in part on the averaged value. In someembodiments, for example, the pixel is assigned to the averaged value ina particular channel. In some embodiments, the first channel ispredetermined (e.g., representing a particular channel corresponding togas sensor data).

FIG. 10 illustrates a process 1000 for assigning a pixel value based onan averaged value of flame sensor data in accordance with at least someexample embodiments of the present disclosure. In some embodiments, theprocess 1000 is embodied by computer program code stored on anon-transitory computer-readable storage medium of a computer programproduct configured for execution to perform the process as depicted anddescribed. Alternatively or additionally, in some embodiments, theprocess 1000 is performed by one or more specially configured computingdevices, such as the apparatus 200 alone or in communication with one ormore other component(s), device(s), system(s), and/or the like. In thisregard, in some such embodiments, the apparatus 200 is speciallyconfigured by computer-coded instructions (e.g., computer programinstructions) stored thereon, for example in the memory 204 and/oranother component depicted and/or described herein and/or otherwiseaccessible to the apparatus 200, for performing the operations asdepicted and described. In some embodiments, the apparatus 200 is incommunication with one or more external apparatus(es), system(s),device(s), and/or the like, to perform one or more of the operations asdepicted and described. For example, the apparatus 200 in someembodiments is in communication with an end-user computing device,sensor(s), alert system(s), and/or the like. For purposes of simplifyingthe description, the process 1000 is described as performed by and fromthe perspective of the apparatus 200.

The process 1000 begins at operation 1002. In some embodiments, theprocess 1000 begins after one or more operations depicted and/ordescribed with respect to any one of the other processes describedherein. For example, in some embodiments as depicted, the process 1000begins after execution of operation 702. In this regard, some or all ofthe process 900 may replace or supplement one or more blocks depictedand/or described with respect to any of the processes described herein.Upon completion of the process 900, the flow of operations mayterminate. Additionally or alternatively, as depicted, upon completionof the process 900 in some embodiments, flow may return to one or moreoperation(s) of another process, such as the operation 704. It will beappreciated that, in some embodiments, the process 900 embodies asub-process of one or more other process(es) depicted and/or describedherein, for example the process 700.

In operation 1002, generate an averaged value by averaging the firstflame sensor data portion and the second flame sensor data portion. Insome embodiments, the apparatus 200 generates the averaged value byaveraging the first flame sensor data portion and the second flamesensor data portion. In some embodiments, the averaged value comprises asimple average of a plurality of flame sensor data portions, includingat least the first flame sensor data portion and the second flame sensordata portion. Alternatively or additionally, in some embodiments, theaveraged value comprises a weighted average of a plurality of flamesensor data portions, including at least the first flame sensor dataportion and the second flame sensor data portion.

In some embodiments, the first flame sensor data portion and the secondflame sensor data portion correspond to sequentially captured samples.For example, the second flame sensor data portion may be captured afterthe first flame sensor data portion by a particular flame sensor. Insome such contexts, the averaged value may be generated corresponding toa different sampling rate than the sampling rates corresponding toindividual first and second flame sensor data portions. Alternatively oradditionally, in some embodiments, the first flame sensor data portionand the second flame sensor data portion are captured by different flamesensors associated with the same environment or sub-region of theenvironment. In some such embodiments, all flame sensor data portionsfrom flame sensors corresponding to the sub-region may be averaged todetermine a value for the sub-region associated with a particularsampling time (e.g., corresponding to samples captured at the sametimestamp or within a particular interval from one another).

In operation 1004, assign at least a first pixel of an additionalchannel of the plurality of additional channels based at least in parton the averaged value. In some embodiments, the flame sensor data isutilized to assign a pixel value for each of a plurality of additionalchannels. For example, different data values of a portion of flamesensor data (e.g., associated with different frequency bands in thecontext of IR flame sensor data for example) may be utilized to assignpixel values to a different channel for each different data value. Insome embodiments, the additional channel to be set by the data valuesfor the particular flame sensor data portion is predetermined.

In some embodiments, for example, the pixel is assigned to the averagedvalue in a particular channel. In some embodiments, the apparatus 200continues to assign pixel values until all pixels for all channels in aparticular data-constructed image have been assigned. Thedata-constructed image is then stored and/or outputted for furtherprocessing, as described herein.

In some embodiments, pixel values for each channel of a plurality ofchannels are assigned in parallel. For example, a first channelassociated with pixel values assigned based at least in part on gassensor data may be assigned in parallel with one or more additionalchannels assigned based at least in part on flame sensor data. In thisregard, the data-constructed image may be formed with improvedefficiency. In other embodiments, pixel values for channels are assignedserially, such that one channel is processed at a time.

FIG. 11 illustrates a process 1100 for assigning pixel values in asequence over a timestamp interval in accordance with at least someexample embodiments of the present disclosure. In some embodiments, theprocess 1100 is embodied by computer program code stored on anon-transitory computer-readable storage medium of a computer programproduct configured for execution to perform the process as depicted anddescribed. Alternatively or additionally, in some embodiments, theprocess 1100 is performed by one or more specially configured computingdevices, such as the apparatus 200 alone or in communication with one ormore other component(s), device(s), system(s), and/or the like. In thisregard, in some such embodiments, the apparatus 200 is speciallyconfigured by computer-coded instructions (e.g., computer programinstructions) stored thereon, for example in the memory 204 and/oranother component depicted and/or described herein and/or otherwiseaccessible to the apparatus 200, for performing the operations asdepicted and described. In some embodiments, the apparatus 200 is incommunication with one or more external apparatus(es), system(s),device(s), and/or the like, to perform one or more of the operations asdepicted and described. For example, the apparatus 200 in someembodiments is in communication with an end-user computing device,sensor(s), alert system(s), and/or the like. For purposes of simplifyingthe description, the process 1100 is described as performed by and fromthe perspective of the apparatus 200.

The process 1100 begins at operation 1102. In some embodiments, theprocess 1100 begins after one or more operations depicted and/ordescribed with respect to any one of the other processes describedherein. For example, in some embodiments as depicted, the process 1100begins after execution of operation 702. In this regard, some or all ofthe process 1100 may replace or supplement one or more blocks depictedand/or described with respect to any of the processes described herein.Upon completion of the process 1100, the flow of operations mayterminate. Additionally or alternatively, as depicted, upon completionof the process 1100 in some embodiments, flow may return to one or moreoperation(s) of another process, such as the operation 704. It will beappreciated that, in some embodiments, the process 1100 embodies asub-process of one or more other process(es) depicted and/or describedherein, for example the process 700.

In operation 1102, the process 1100 assigns a first pixel value of achannel based at least in part on a first sensor data portion of thefirst time series of sensor data portions, the first sensor data portioncorresponding to a first timestamp. In this regard, the first sensordata may be captured associated with a particular sample interval, forexample determined based at least in part on a sampling rate for thesensor that captured the particular first sensor data portion. Theparticular sensor(s) that captured the first sensor data portion maycontinue to capture subsequent sensor data portions, each associatedwith a subsequent timestamp. In this regard, the apparatus 200 mayreceive any number of sensor data portions from a particular sensor,each associated with a particular timestamp or timestamp interval.

In operation 1104, the process 1100 assigns each subsequent pixel valueof the first channel based at least in part on a next gas sensor dataportion associated with each subsequent timestamp. In some embodiments,the apparatus 200 assigns pixel values to pixels sequentially, forexample starting from a determinable starting pixel and progressinglinearly in a particular direction (e.g., along an x-axis or a y-axis ofthe pixels). Alternatively or additionally, in some embodiments theapparatus 200 determines a subsequent pixel to be assigned based onanother determinable pattern, pixel selection algorithm, and/or thelike. For example, in some embodiments, the apparatus 200 assigns pixelsradially beginning from a determinable starting pixel. The pixels valuesmay be assigned based at least in part on the sensor data portionsreceived in chronological order, based at least in part on the timestampand/or timestamp intervals associated with such sensor data portion(s).

FIG. 12 illustrates a process 1200 for generating a data-constructedimage based at least in part on a plurality of sub-images in accordancewith at least some example embodiments of the present disclosure. Insome embodiments, the process 1200 is embodied by computer program codestored on a non-transitory computer-readable storage medium of acomputer program product configured for execution to perform the processas depicted and described. Alternatively or additionally, in someembodiments, the process 1200 is performed by one or more speciallyconfigured computing devices, such as the apparatus 200 alone or incommunication with one or more other component(s), device(s), system(s),and/or the like. In this regard, in some such embodiments, the apparatus200 is specially configured by computer-coded instructions (e.g.,computer program instructions) stored thereon, for example in the memory204 and/or another component depicted and/or described herein and/orotherwise accessible to the apparatus 200, for performing the operationsas depicted and described. In some embodiments, the apparatus 200 is incommunication with one or more external apparatus(es), system(s),device(s), and/or the like, to perform one or more of the operations asdepicted and described. For example, the apparatus 200 in someembodiments is in communication with an end-user computing device,sensor(s), alert system(s), and/or the like. For purposes of simplifyingthe description, the process 1200 is described as performed by and fromthe perspective of the apparatus 200.

The process 1200 begins at operation 1202. In some embodiments, theprocess 1200 begins after one or more operations depicted and/ordescribed with respect to any one of the other processes describedherein. For example, in some embodiments as depicted, the process 1200begins after execution of operation 702. In this regard, some or all ofthe process 1200 may replace or supplement one or more blocks depictedand/or described with respect to any of the processes described herein.Upon completion of the process 1200, the flow of operations mayterminate. Additionally or alternatively, as depicted, upon completionof the process 1200 in some embodiments, flow may return to one or moreoperation(s) of another process, such as the operation 704. It will beappreciated that, in some embodiments, the process 1200 embodies asub-process of one or more other process(es) depicted and/or describedherein, for example the process 700.

In operation 1202, the process 1200 generates a first sub-imagecorresponding to the first environment sub-region based at least in parton the first gas sensor data portion and the first flame sensor dataportion. In some embodiments, for example, the apparatus 200 generates afirst sub-image by generating a plurality of channels based at least inpart on the first gas data portion and the first flame sensor dataportion. In this regard, the first gas sensor data portion may beutilized to assign a pixel value for a first channel of the sub-image,and the first flame sensor data portion may be utilized to assign apixel value for a plurality of additional channels of the sub-image(e.g., based at least in part on different data values for the firstportion of flame sensor data). It will be appreciated that each channelof the sub-image may in and of itself embody a secondary sub-image(e.g., the secondary sub-image comprising a single channel).

In operation 1204, the process 1200 generates a second sub-imagecorresponding to the second environment region based at least in part onthe second gas data portion and the second flame data portion. It willbe appreciated that the second sub-image may be similarly assigned to anew image (e.g., the second sub-image) in the same manner as describedwith respect to operation 1202. For example, in some embodiments theapparatus 200 generates the second sub-image by generating a pluralityof channel based at least in part on the second gas data portion and thesecond flame sensor data portion, such as by assigning pixel values tochannels of the second sub-image.

In operation 1206, the process 1200 generates the data-constructed imageby assigning a first portion of the data-constructed image to the firstsub-image and assigning a second portion of the data-constructed imageto the second sub-image. In some embodiments, the first portion of thedata-constructed image corresponds to a first determinable pixelsub-region. Additionally or alternatively, in some embodiments thesecond portion of the data-constructed image corresponds to a seconddeterminable pixel sub-region. In some embodiments the apparatus 200maintains a predefined schema of pixel sub-regions associated withparticular sub-regions of an environment, for example the firstenvironment sub-region and the second environment sub-region. In thisregard, the apparatus 200 may utilize the sub-image generated utilizingsensor data captured from the particular sub-region to assign thegenerated sub-image to the predefined pixel sub-region corresponding tothe environment sub-region. In this regard, it will be appreciated thatthe first sub-image and the second sub-image may be associated withdifferent pixel sub-regions and thus utilized to assign different pixelsof the entire data-constructed image. Additionally or alternatively, insome embodiments the apparatus 200 appends the first sub-image with thesecond sub-image, stitches the first sub-image with the secondsub-image, or otherwise combines the first sub-image with the secondsub-image to generate the data-constructed image including the pixelvalues of both sub-images.

FIG. 13 illustrates a process 1300 for generating a warning signal inaccordance with at least some example embodiments of the presentdisclosure. In some embodiments, the process 1300 is embodied bycomputer program code stored on a non-transitory computer-readablestorage medium of a computer program product configured for execution toperform the process as depicted and described. Alternatively oradditionally, in some embodiments, the process 1300 is performed by oneor more specially configured computing devices, such as the apparatus200 alone or in communication with one or more other component(s),device(s), system(s), and/or the like. In this regard, in some suchembodiments, the apparatus 200 is specially configured by computer-codedinstructions (e.g., computer program instructions) stored thereon, forexample in the memory 204 and/or another component depicted and/ordescribed herein and/or otherwise accessible to the apparatus 200, forperforming the operations as depicted and described. In someembodiments, the apparatus 200 is in communication with one or moreexternal apparatus(es), system(s), device(s), and/or the like, toperform one or more of the operations as depicted and described. Forexample, the apparatus 200 in some embodiments is in communication withan end-user computing device, sensor(s), alert system(s), and/or thelike. For purposes of simplifying the description, the process 1300 isdescribed as performed by and from the perspective of the apparatus 200.

The process 1300 begins at operation 1302. In some embodiments, theprocess 1300 begins after one or more operations depicted and/ordescribed with respect to any one of the other processes describedherein. For example, in some embodiments as depicted, the process 1300begins after execution of operation 710. In this regard, some or all ofthe process 1300 may replace or supplement one or more blocks depictedand/or described with respect to any of the processes described herein.Upon completion of the process 1300, the flow of operations mayterminate. Additionally or alternatively, as depicted, upon completionof the process 1300 in some embodiments, flow may return to one or moreoperation(s) of another process, such as the operation 702. It will beappreciated that, in some embodiments, the process 1300 embodies asub-process of one or more other process(es) depicted and/or describedherein, for example the process 700.

In operation 1302, the process 1300 determines the explosion predictiondata satisfies a warning threshold. In some embodiments, the warningthreshold indicates a cutoff that, if satisfies, indicates a warningregarding a likelihood of explosion in at least a sub-region of anenvironment is necessary. In some embodiments, the apparatus 200determines the explosion prediction data satisfies the warning thresholdby at least comparing the explosion prediction data to the threshold. Insome embodiments, the warning threshold embodies a maximum probabilitythat, if exceeded or otherwise satisfied, indicates a warning should beoutputted. In some embodiments, the warning threshold embodies one ormore discrete data values, for example that indicate a dangerousenvironment. It will be appreciated that in some embodiments, thecomparison may be performed in any manner that determines whether theexplosion prediction data indicates one or more region(s) of theenvironment are dangerous due to an unacceptable risk of explosionwithin said region(s).

In operation 1304, the process 1300 in response to determining theexplosion prediction data satisfies the threshold, generates a warningsignal. In some embodiments, the warning signal embodies data thatfacilitates output of at least one alert. The warning signal may triggergeneration, transmission, and/or output of an audio alert, visual alert,user interface alert, pop-up, siren, user device notification, email,and/or the like. In some embodiments, a plurality of warning signals aregenerated that cause output of a plurality of alerts. For example, inone example context, one or more warning signal(s) is/are generated thatfacilitate transmission of an alert to a user device associated with anemergency response and/or repair team associated with a particularenvironment or sub-region thereof, and/or that activates an alert systemin the environment and/or sub-region thereof that is determined at riskof explosion based at least in part on the explosion prediction data.

CONCLUSION

Although an example processing system has been described above,implementations of the subject matter and the functional operationsdescribed herein can be implemented in other types of digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described hereincan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter describedherein can be implemented as one or more computer programs, i.e., one ormore modules of computer program instructions, encoded on computerstorage medium for execution by, or to control the operation of,information/data processing apparatus. Alternatively, or in addition,the program instructions can be encoded on an artificially-generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, which is generated to encode information/datafor transmission to suitable receiver apparatus for execution by aninformation/data processing apparatus. A computer storage medium can be,or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described herein can be implemented as operationsperformed by an information/data processing apparatus oninformation/data stored on one or more computer-readable storage devicesor received from other sources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a repositorymanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor information/data (e.g., one or more scripts stored in a markuplanguage document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described herein can be performed by oneor more programmable processors executing one or more computer programsto perform actions by operating on input information/data and generatingoutput. Processors suitable for the execution of a computer programinclude, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions andinformation/data from a read-only memory or a random access memory orboth. The essential elements of a computer are a processor forperforming actions in accordance with instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive information/datafrom or transfer information/data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto-optical disks, oroptical disks. However, a computer need not have such devices. Devicessuitable for storing computer program instructions and information/datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described herein can be implemented on a computer having adisplay device, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information/data to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described herein can be implemented ina computing system that includes a back-end component, e.g., as aninformation/data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described herein, or any combination of one or more suchback-end, middleware, or front-end components. The components of thesystem can be interconnected by any form or medium of digitalinformation/data communication, e.g., a communication network. Examplesof communication networks include a local area network (“LAN”) and awide area network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits information/data (e.g., an HTML page) toa client device (e.g., for purposes of displaying information/data toand receiving user input from a user interacting with the clientdevice). Information/data generated at the client device (e.g., a resultof the user interaction) can be received from the client device at theserver.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anydisclosures or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular disclosures.Certain features that are described herein in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults.

What is claimed is:
 1. A computer-implemented method comprising:receiving a plurality of gas sensor data and a plurality of flame sensordata; generating a data-constructed image comprising a plurality ofchannels, the plurality of channels comprising at least a first channelassigned based at least in part on the plurality of gas sensor data andat least one additional channel assigned based at least in part on theplurality of flame sensor data; and generating explosion prediction databy applying at least a portion of the data-constructed image to aprediction model, wherein the prediction model generates the explosionprediction data based at least in part on explosion feature datadetermined from at least the plurality of channels corresponding to atleast a portion of the data-constructed image.
 2. Thecomputer-implemented method of claim 1, the computer-implemented methodfurther comprising: applying the data-constructed image to a computervision model that identifies at least the portion of thedata-constructed image determined associated with at least one explosioncontribution level indicating presence of at least one explosioncontributing factor in an environment; and extracting the portion of thedata-constructed image from the data-constructed image, wherein theprediction model only processes the extracted portion of thedata-constructed image.
 3. The computer-implemented method of claim 1,wherein the plurality of gas sensor data comprises at least a first gassensor data portion and a second gas sensor data portion captured via afirst gas sensor, and wherein the plurality of gas sensor data comprisesa first gas sensor data portion associated with a first sampling rate,and wherein the plurality of flame sensor data comprises at least afirst flame sensor portion captured via a first flame sensor associatedwith a second sampling rate, wherein the first sampling rate is fasterthan the second sampling rate, and wherein generating thedata-constructed image comprises: generating an averaged value byaveraging the first gas sensor data portion and the second gas sensordata portion; and assigning at least a first pixel of the first channelbased at least in part on the averaged value.
 4. Thecomputer-implemented method of claim 1, wherein the plurality of gassensor data comprises at least a first gas sensor data portion capturedvia a first gas sensor, and wherein the plurality of gas sensor datacomprises a first gas sensor data portion associated with a firstsampling rate, and wherein the plurality of flame sensor data comprisesat least a first flame sensor portion and a second flame sensor portioncaptured via a first flame sensor associated with a second samplingrate, wherein the second sampling rate is faster than the first samplingrate, and wherein generating the data-constructed image comprises:generating an averaged value by averaging the first flame sensor dataportion and the second flame sensor data portion; and assigning at leasta first pixel of an additional channel of the plurality of additionalchannels based at least in part on the averaged value.
 5. Thecomputer-implemented method of claim 1, wherein the plurality of gassensor data comprises a first time series of gas sensor data portionscaptured via at least one gas sensor, and wherein generating thedata-constructed image comprises: assigning a first pixel value of thefirst channel based at least in part on a first gas sensor data portionof the first time series of gas sensor data portions corresponding to afirst timestamp; and assigning each subsequent pixel value of the firstchannel based at least in part on a next gas sensor data portionassociated with each subsequent timestamp.
 6. The computer-implementedmethod of claim 1, wherein the plurality of flame sensor data comprisesat least first band range data, second band range data, and third bandrange data, wherein the at least one additional channel comprises asecond channel, a third channel, and a fourth channel, and whereingenerating the data-constructed image comprises: assigning the secondchannel based at least in part on the first band range data; assigningthe third channel based at least in part on the second band range data;and assigning the fourth channel based at least in part on the thirdband range data.
 7. The computer-implemented method of claim 1, thecomputer-implemented method further comprising: applying thedata-constructed image to a computer vision model that at leastdetermines an explosion contribution level; and determining theexplosion contribution level satisfies a threshold, wherein thegenerating the explosion likelihood data is initiated in response todetermining that the explosion contribution level satisfies thethreshold.
 8. The computer-implemented method of claim 1, wherein theplurality of gas sensor data is collected via a plurality of gassensors.
 9. The computer-implemented method of claim 1, wherein theplurality of flame sensor data is collected via a plurality of flamesensors.
 10. The computer-implemented method of claim 1, wherein theplurality of gas sensor data comprises at least a first gas data portionassociated with a first gas sensor corresponding to a first environmentregion and a second gas data portion associated with a second gas sensorcorresponding to a second environment region, and wherein the pluralityof flame sensor data comprises a first flame data portion associatedwith a first flame sensor corresponding to the first environment regionand a second flame data portion associated with a second flame dataportion corresponding to the second environment region, whereingenerating the data-constructed image comprises: generating a firstsub-image corresponding to the first environment region based at leastin part on the first gas data portion and the first flame data portion;generating a second sub-image corresponding to the second environmentregion based at least in part on the second gas data portion and thesecond flame data portion; and generating the data-constructed image byassigning a first portion of the data-constructed image to the firstsub-image and assigning a second portion of the data-constructed imageto the second sub-image.
 11. The computer-implemented method of claim 1,wherein the data-constructed image comprises a plurality of sub-image,each sub-image corresponding to an assigned pixel sub-region of thedata-constructed image.
 12. The computer-implemented method of claim 1,wherein the explosion prediction data comprises a data value indicatinga probability of an explosion.
 13. The computer-implemented method ofclaim 1, the computer-implemented method further comprising: determiningthe explosion prediction data satisfies a threshold by at leastcomparing the explosion prediction data to the threshold; and inresponse to determining the explosion prediction data satisfies thethreshold, generating a warning signal.
 14. The computer-implementedmethod of claim 1, wherein the prediction model comprises a speciallytrained machine learning model.
 15. A computing apparatus comprising: atleast one processor; and at least one memory storing instructions that,when executed by the at least one processor, configure the apparatus to:receive a plurality of gas sensor data and a plurality of flame sensordata; generate a data-constructed image comprising a plurality ofchannels, the plurality of channels comprising at least a first channelassigned based at least in part on the plurality of gas sensor data andat least one additional channel assigned based at least in part on theplurality of flame sensor data; and generate explosion prediction databy applying at least a portion of the data-constructed image to aprediction model, wherein the prediction model generates the explosionprediction data based at least in part on explosion feature datadetermined from at least the plurality of channels corresponding to atleast a portion of the data-constructed image.
 16. The apparatus ofclaim 15, wherein the plurality of flame sensor data comprises at leastfirst band range data, second band range data, and third band rangedata, wherein the at least one additional channel comprises a secondchannel, a third channel, and a fourth channel, and wherein to generatethe data-constructed image the apparatus is configured to: assign thesecond channel based at least in part on the first band range data;assign the third channel based at least in part on the second band rangedata; and assign the fourth channel based at least in part on the thirdband range data.
 17. The apparatus of claim 15, the instructions furtherconfigure the apparatus to: apply the data-constructed image to acomputer vision model that at least determines an explosion contributionlevel; and determine the explosion contribution level satisfies athreshold, wherein the generating the explosion likelihood data isinitiated in response to determining that the explosion contributionlevel satisfies the threshold.
 18. The computing apparatus of claim 15,wherein the plurality of gas sensor data comprises at least a first gasdata portion associated with a first gas sensor corresponding to a firstenvironment region and a second gas data portion associated with asecond gas sensor corresponding to a second environment region, andwherein the plurality of flame sensor data comprises a first flame dataportion associated with a first flame sensor corresponding to the firstenvironment region and a second flame data portion associated with asecond flame data portion corresponding to the second environmentregion, wherein generating the data-constructed image comprises:generate a first sub-image corresponding to the first environment regionbased at least in part on the first gas data portion and the first flamedata portion; generate a second sub-image corresponding to the secondenvironment region based at least in part on the second gas data portionand the second flame data portion; and generate the data-constructedimage by assigning a first portion of the data-constructed image to thefirst sub-image and assigning a second portion of the data-constructedimage to the second sub-image.
 19. A non-transitory computer-readablestorage medium, the computer-readable storage medium includinginstructions that when executed by at least one processor, cause the atleast one processor to: receive a plurality of gas sensor data and aplurality of flame sensor data; generate a data-constructed imagecomprising a plurality of channels, the plurality of channels comprisingat least a first channel assigned based at least in part on theplurality of gas sensor data and at least one additional channelassigned based at least in part on the plurality of flame sensor data;and generate explosion prediction data by applying at least a portion ofthe data-constructed image to a prediction model, wherein the predictionmodel generates the explosion prediction data based at least in part onexplosion feature data determined from at least the plurality ofchannels corresponding to at least a portion of the data-constructedimage.
 20. The computer-readable storage medium of claim 19, wherein theplurality of gas sensor data comprises at least a first gas data portionassociated with a first gas sensor corresponding to a first environmentregion and a second gas data portion associated with a second gas sensorcorresponding to a second environment region, and wherein the pluralityof flame sensor data comprises a first flame data portion associatedwith a first flame sensor corresponding to the first environment regionand a second flame data portion associated with a second flame dataportion corresponding to the second environment region, whereingenerating the data-constructed image comprises: generate a firstsub-image corresponding to the first environment region based at leastin part on the first gas data portion and the first flame data portion;generate a second sub-image corresponding to the second environmentregion based at least in part on the second gas data portion and thesecond flame data portion; and generate the data-constructed image byassigning a first portion of the data-constructed image to the firstsub-image and assigning a second portion of the data-constructed imageto the second sub-image.